Autonomous vehicles may facilitate efficient transportation in coming times. The autonomous vehicle may be capable of sensing the dynamic changing environment, and of navigating without any human intervention. The autonomous vehicle may typically employ a navigation path generated on a highresolution navigation map (e.g., a light detection and ranging (LIDAR) map) for performing autonomous navigation. The path planning of the autonomous vehicle therefore depends on accuracy of the navigation path, which in turn depends on completeness of the navigation map of the territory where the vehicle may eventually run.
However, many a times, while creating the navigation map, information captured is not accurate and therefore the map is incomplete. For example, in some scenarios, non-drivable area just beside the road (e.g., pedestrian area, cycling track, etc.) does not get detected on the navigation map due to negligible number of pedestrians or cyclists, absence of segregating structure, and/or other reasons. Further, many a times, information captured and therefore the navigation map is outdated. For example, a temporary or a permanent structure (e.g., a segregating structure) may have come up since the last creation of the navigation map. Maintaining and updating the mapped locality with all its continuous changes may require effort and may also involve cost.
The navigation path generated based on such incomplete or outdated navigation map may therefore not be accurate for performing autonomous navigation. For example, in some occasions, the inaccurate navigation path generated on the incomplete or outdated navigation map may result in the autonomous vehicle being very near to the potential hitting zone making the navigation unsafe. Thus, existing techniques to provide high precision map and accurate navigation path for autonomous vehicle are effort and resource intensive, and involve huge cost.